Large Language Models Can Take False First Steps at Inference-time Planning

2026-02-03

AI Summary

This work proposes a Bayesian interpretive framework to explain the myopic and inconsistent planning behaviors often exhibited by large language models during reasoning, which contrast with their sequence-level planning capabilities observed during training. The study attributes this discrepancy to an internal linguistic distribution shift induced by the accumulation of self-generated context. Through controlled text generation experiments and tracking of contextual evolution, the authors demonstrate that model planning is constrained under human-provided prompts but enhanced when leveraging self-generated context. Furthermore, under a conditional self-generation setup, initial planning biases are significantly reduced. These findings offer both theoretical insight and empirical grounding for understanding and improving the planning mechanisms underlying model-based reasoning.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Large language models (LLMs) have been shown to acquire sequence-level planning abilities during training, yet their planning behavior exhibited at inference time often appears short-sighted and inconsistent with these capabilities. We propose a Bayesian account for this gap by grounding planning behavior in the evolving generative context: given the subtle differences between natural language and the language internalized by LLMs, accumulated self-generated context drives a planning-shift during inference and thereby creates the appearance of compromised planning behavior. We further validate the proposed model through two controlled experiments: a random-generation task demonstrating constrained planning under human prompts and increasing planning strength as self-generated context accumulates, and a Gaussian-sampling task showing reduced initial bias when conditioning on self-generated sequences. These findings provide a theoretical explanation along with empirical evidence for characterizing how LLMs plan ahead during inference.

Problem

• Large Language Models
• Inference-time Planning
• Planning Behavior
• Self-generated Context
• Planning Shift

Innovation

• inference-time planning
• Bayesian modeling
• self-generated context
• planning shift
• large language models